The present invention relates to disease management in general and, more particularly, to methods and apparatus for improving patients"" compliance with prescriptions for medication that they are given to improve a disease condition.
It is quite common in the treatment of a disease condition for a health care provider to prescribe certain medications for the treatment of the condition. The medications must be taken in specific doses at particular times of day over a course of time in order to insure that they have the maximum effect on the health condition of the patient. In addition, the doctor or other health care provider may prescribe other courses of conduct to effect the disease or condition, e.g., diets, exercise or lifestyle changes. Thus, xe2x80x9ccompliancexe2x80x9d as used herein, is interchangeable with adherence, and means the extent to which a person""s behavior in terms of medications, following diets, or executing lifestyle changes coincides with medical and health advice.
The patient is usually responsible for compliance with the prescription. If the patient fails to comply with the prescription, it is likely that the condition will not improve, or at least not improve as quickly as possible. This, of course, results in an adverse consequence for the patient and his family. Also, the continued poor health condition of the patient has a negative effect on the patient""s employer, which in turn has a negative effect on the economy in general because the patient may not be able to work at all, or to work as efficiently.
Failure to take the medication as prescribed is typically due to the fact that the patient has not filled the prescription at a pharmacy. This results in a loss of income to the pharmacy. If the patient takes only part of the prescribed medication, the results may not be satisfactory, and the patient and health care provider may assume that the medication is ineffective. If this view is shared with others, it can have a negative impact on the sale of the medication, which could harm the manufacturer of the medication unfairly.
Patients who do not take their prescribed medication also place additional burdens on the health care system in which the patient is enrolled because they stay ill longer, which increases the medical services that must be provided to the patient and the attendant cost. The health care system may be a private medical.plan, perhaps supported by an employer, or the government. Where the medical plan is a managed care organization (xe2x80x9cMCOsxe2x80x9d) , such as an HMO, a PPOs, etc., this burden may be critical, because such organizations are required to manage care in a cost effective and high quality way to be competitive. Also, when a patient remains ill longer than necessary, they may get worse and require emergency room treatment, which is perhaps the most expensive way to handle a disease condition. Further the ineffectively treated condition may lead to other and more serious complications, which again reduces the quality of life of the patient, reduces the patient""s ability to contribute work effort to his employer, and generally increases costs for treatment.
It is estimated by the National Association of Chain Drug Stores (xe2x80x9cNACDSxe2x80x9d) that 50% of all prescriptions dispensed in the U.S. are not taken correctly, and that non-compliance with prescription medications costs Americans between $50 billion and $100 billion each year in increased hospitalization and long-term health complications. The NACDS also notes that pharmacist intervention improves compliance and outcomes, and consequently lowers health care costs.
Disease Management is a new area of medicine which focuses on organizing the treatment of patients according to the disease or condition the patient may have so that services are delivered to the patient in a way that is most effective, and utilizes the most appropriate and cost effective service delivered by the most appropriate health care provider needed at the time. One aspect of disease management is to assure, as best as possible, that the patient complies with the prescription. However, in most cases, because the patient has the primary responsibility for this, it is difficult to track this compliance and intervene, e.g. when a course of medication is not being followed. Thus, it would be helpful if indications of lack of compliance were available, so interventions could be effective to get a person to comply with his prescription.
One indication of lack of compliance is whether a patient refills a prescription on time. This information is known to the pharmacy where the prescription was first filled, but may not be generally to the health care system in which the patient is involved. Even if known, there is not a well known system for utilizing this information to obtain patient compliance, except perhaps for the individual pharmacy to send the patient a notice when he does not return on time to fill the prescription.
It would be a great benefit in assuring compliance with medication prescriptions if a particular patient""s behavior in this regard could be predicted at some time well before too much.time has passed since the due date for the refill of the prescription. It would be of even more benefit if a prediction of compliance were effective in cases where only a single prescription is required, and there is no intent to refill the prescription. Then some intervention could be taken at the time the medication is prescribed in order to improve the likelihood that the patient will take the medication as prescribed. It would also be beneficial to know which of several interventions are most effective in terms of encouraging compliance.
The present invention is directed to improving the ability of organizations to improve the compliance of patients with prescriptions by predicting, based on information available upon the filling of the prescription, which type of intervention will be most effective at getting patients to follow their prescriptions. As a result, the organization can intervene with the proper type of intervention very early. Further, one aspect of the invention is directed to predicting which patients are most likely to fail to comply with their prescription, so that intervention can be directed at these patients that are at high risk of non-compliance at the time the prescription is given. The intervention may be in the form of educational information, reminders, etc. Further, by being able to predict those most likely not to comply, the organization can focus its compliance intervention activities on those patients. This allows for efficient use of the organization""s resources, since interventions do not have to be sent to those who are very likely to comply anyway. Further, various intervention techniques can be employed and their effectiveness tracked with the present invention, in a xe2x80x9cchampion-challengerxe2x80x9d scenario, so that compliance efforts can be improved over time.
According to the present invention, the predictions of the interventions that will be most effective are made by creating electronic records for each patient and appending additional demographic information to each record. This information is obtained from commercial databases. Typically, the information includes the demographic information and can also include (1) pharmacy information, e.g., information typically received by a pharmacy when a prescription is placed; (2) clinical information about the patient, e.g., information from examinations conducted by health care providers (such as blood pressure, body temperature, pulse rate, respiration rate, height, weight, EKG, etc.); (3) patient reports, e.g., questions asked of patients by the health care provider or derived from surveys of patients by any number of organizations; and (4) medical records of diagnoses and treatment (e.g., associated ICD 9 codes, CPT-4 codes, etc.) With this information a model of patient response to various intervention messages is formed using regression analysis in which compliance with a prescription is the dependent variable, and a group of particular intervention messages and the demographic information are the independent variables. The result is a reasonable number of demographic clusters which respond best to certain of the messages. Each cluster has a model defined by the regression analysis which is in the form of a probability equation having the independent variables multiplied by weighting factors which represent the relative significance of the variable in predicting the result.
The most effective intervention messages are so-called xe2x80x9cchampionxe2x80x9d messages, and they are the ones used to intervene with patients meeting the criterion for the related demographic cluster. In addition, the patient prescription or pharmacy information, and the demographic information (collectively xe2x80x9cpatient dataxe2x80x9d) can be regressed with the compliance information to obtain a prediction of which patients are likely to comply with their prescription and which ones are not. Thus, the champion messages for a cluster can be utilized with those patients in the cluster who are most likely not to comply, thereby making the intervention most efficient.
New xe2x80x9cchallengerxe2x80x9d intervention messages can be created. xe2x80x9cChallengerxe2x80x9d messages are messages created to take the place of the champion messages if they prove more effective. These challenger messages can be sent to selected groups of patients and additional compliance information collected. This additional information and the prior patient data is then made the subject of another regression analysis to see if the challenger messages are more effective with one of the clusters than the current champion message for that cluster. If a challenger proves more effective by creating more compliance, it becomes the new champion. Further, the regression analysis can be re-run with the champion and challenger messages in combination with the demographic information to establish new clusters, if they result in a higher compliance ratio.
As additional data, including compliance information, is obtained over time, the weighting of the various components of the patient data can be dynamically modified to improve the accuracy of the models as a means for predicting the best message for a cluster and the patients who are most likely to fail to comply. If the model is based on prescription intake data at the pharmacy, or on data available even earlier when the health care provider writes the prescription, the patient""s compliance with the prescription can be predicted as a probability early in the patient""s treatment, allowing the maximum time for intervention.
In an illustrative embodiment of the invention, an organization, e.g., a pharmacy, a chain of pharmacies, or a third party organization acting on their behalf, collects from customers (i.e., patients) traditional data on a customer or patient being treated for some disease or condition, sometimes referred to as xe2x80x9cpharmacy dataxe2x80x9d or xe2x80x9cprescription data.xe2x80x9d This pharmacy data, which includes information identifying the patient and the medication prescribed for him, is converted into electronic form, e.g., with a data input terminal. This electronic data is stored in a database as patient records for later use.
In carrying out the invention, the procedure is to associate this patient data or information with (1) the effectiveness of a particular intervention, and/or (2) the probability of patient compliance with the medication prescription. This may be accomplished by a regression analysis carried out in a programmed digital computer which can access the patient records in the database. Each data element of patient data and various intervention messages are regressed in regard to the patient""s compliance with the prescription, as best determined by the organization. The analysis suggests models of patient behavior in the form of the interventions that work best in effecting compliance, as well as models that predict compliance of a patient with a prescription or regardless of intervention. The models are in the form of probability equations which are the sum of each variable or element of the information multiplied by a weighting factor for that variable, as determined by the regression analysis.
It has been determined, however, that a more accurate model can be derived if additional variables are used to group patients into demographic clusters which are somewhat more homogeneous than the population at large. This is done through the use of additional demographic variables. Such additional variables can be derived from the basic patient information. For example, the patient""s address, particularly the zip code, can be used to access databases of demographic information related thereto, e.g., average income, ethnic background, etc. of people living in that zip code. The purpose is to predict, based on past results, which intervention works best with which demographic cluster. Thus, when a new patient is to be subjected to an intervention, it is first determined which demographic cluster the patient belongs to, then the intervention most effective with patients in that cluster is sent to that patient.
Further, the most predictive clusters can be determined in a shorter period of time if logical choices are made of variables and combinations of variables which define the clusters. Some variables will have no predictive ability and will be discarded. The higher the predictive value, the larger the weighting factor. Further, assumptions can be made about which variables are the most important. The regression analysis can thus be made with regard to these variables first. If these variables are in fact important with regard to predicting compliance, the pharmacy may have some idea of the reasons for compliance or lack thereof, and can craft an intervention based on this information.
The models for the most effective intervention for a cluster and/or patient compliance, are stored as equations in the digital computer memory. Then as each patient""s prescription data is input to the terminal, the computer uses the model equations to associate the patient with a cluster and/or create a score for the patient. The computer may be the same one that created the model or another one. For example, the model may be created and then stored on personal computers located at the office of the health care provider or the pharmacy, which are also used to input patient information or prescription information. As an alternative the model could be on a remote computer in electronic communication with a personal computer at the pharmacy or office of the health care provider.
Depending on the cluster to which the patient is assigned by the computer, the patient will receive an intervention message that has been shown by the regression analysis to be most effective with members of that cluster. The message may be automatically generated and sent by the computer. If a patient compliance model has been created, and the application of the patient""s information to this compliance model results in a patient""s score that indicates a high probability that the patient will not comply with the prescription, the pharmacy, MCO, drug manufacturer or a third party representative of such organizations which service the patient, can intervene first with that patient to get him to follow the prescription. If resources are low, it may be that only high risk patients in a cluster receive the intervention.
The intervention may be by letter, fax, e-mail, Internet or a store and forward system (e.g., by pager). It may also be a phone call. While the intervention is typically a message of some sort, it may take some other form, e.g., an incentive awarded upon compliance. Regardless of the type of intervention, it may be automatically created and sent by apparatus used by the pharmacy, MCO, drug manufacturer or an organization administering the system for one or all of them. Further, the call or letter may be made to, or sent to, the patient, a spouse of the patient, the doctor of the patient and/or the employer of the patient, depending on which target of the intervention that experience has shown to produce the best results. The intervention information may be educational, e.g., warning of the consequences of not strictly following the prescription, and/or a notice that the medication needs to be taken or the prescription refilled.
The pharmacy or MCO can continuously collect data on patients and periodically use it to redo the regression analysis in an attempt to improve the models by basing them on more data. Also, the type of intervention can be a varied. Thus, the current best intervention, i.e., the champion, can be tested with a challenger message, to see if it can displace the champion.
Therefore, the present invention is directed to intervention processes focused on the objective of increasing the patient""s compliance with a prescription. The intervention processes form a seamlessly integrated system of patient tracking, education and continuous quality improvements driven by sophisticated data and behavior analyses.